Hyperspectral Image Classification Method Based on Adaptive Manifold Filtering

Jianshang Liao; Liguo Wang; Siyuan Hao

doi:10.3788/LOP55.041010

Journals >Laser & Optoelectronics Progress >Volume 55 >Issue 4 >Page 041010 > Article

Laser & Optoelectronics Progress
Vol. 55, Issue 4, 041010 (2018)

Hyperspectral Image Classification Method Based on Adaptive Manifold Filtering

Jianshang Liao^1、*, Liguo Wang¹, and Siyuan Hao¹

Author Affiliations

¹ College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China

¹ School of Communication and Electronic Engineering, Qingdao University of Technology, Qingdao, Shandong 266520, China

¹ School of Rail Transit, Guangdong Communication Polytechnic, Guangzhou, Guangdong 510650, China

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DOI: 10.3788/LOP55.041010 Cite this Article Set citation alerts

Jianshang Liao, Liguo Wang, Siyuan Hao. Hyperspectral Image Classification Method Based on Adaptive Manifold Filtering[J]. Laser & Optoelectronics Progress, 2018, 55(4): 041010 Copy Citation Text

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Fig. 1. Original spectrum and filtering results of Indian Pines data sets. (a) 10th band; (b) 80th band; (c) 120th band; (d) 180th band

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Optimization for manifold filtering coefficient of Indian Pines data sets. (a) Spatial deviation coefficient σs; (b) range deviation coefficient σr

Fig. 2. Optimization for manifold filtering coefficient of Indian Pines data sets. (a) Spatial deviation coefficient σs; (b) range deviation coefficient σr

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Fig. 3. Flow of AMF-SVM

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Fig. 4. Classification of Indian Pines data sets. (a) Ground truth; (b) SVM, OA is 80.93%; (c) SVM-PCA, OA is 80.46%; (d) GBF-SVM, OA is 82.82%; (e) BF-SVM, OA is 88.99%; (f) GDF-SVM, OA is 91.08%; (g) EPF-B-g, OA is 92.99%; (h) EPF-G-g, OA is 92.83%; (i) IFRF, OA is 93.64%; (j) AMF-SVM, OA is 95.16%

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Fig. 5. Classification for Pavia University. (a) Ground truth;(b) SVM, OA is 84.80%; (c) SVM-PCA, OA is 83.95%; (d) GBF-SVM, OA is 85.20%; (e) BF-SVM, OA is 89.03%; (f) GDF-SVM, OA is 94.20%; (g) EPF-B-g, OA is 91.29%; (h) EPF-G-g, OA is 91.68%; (i) IFRF, OA is 95.31%; (j) AMF-SVM, OA is 97.92%

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Fig. 6. Charts of OA and Kappa coefficient with different training samples. (a) Indian Pines; (b) Pavia University

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Fig. 7. OA and Kappa coefficient for different classification methods. (a) 1% training sample for Indian Pins; (b) 0.1% training sample for Pavia University

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Fig. 8. Optimization for hyperspectral classification of adaptive manifold filtering. (a) Indian Pins; (b) Pavia University

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Groundtruth	SumsampleNo.	TrainsampleNo. /%	TestsampleNo. /%	SVM /%	SVM-PCA /%	GBF-SVM /%	BF-SVM /%	GDF-SVM /%	EPF-B-g /%	EPF-G-g /%	IFRF /%	AMF-SVM /%
Alfalfa	54	7	93	83.57	78.89	88.83	91.86	91.10	95.58	94.78	91.34	92.24
Corn-no till	1434	7	93	71.50	71.08	76.46	84.28	87.25	91.57	91.39	91.23	96.95
Corn-min till	834	7	93	70.63	72.02	70.38	88.93	91.67	87.34	87.54	84.64	97.90
Corn	234	7	93	44.19	41.48	51.61	57.98	66.21	62.57	61.39	86.22	87.16
Grass/pasture	497	7	93	89.90	89.12	88.96	92.29	93.60	95.48	94.82	93.96	93.58
Grass/trees	747	7	93	94.79	94.63	95.15	96.79	96.86	99.79	99.50	98.10	97.40
Grass/pasture-mowed	26	7	93	53.91	53.27	66.50	62.45	64.31	54.19	62.80	88.13	76.67
Hay-windrowed	489	7	93	97.16	96.18	99.56	98.33	97.47	100.0	100.0	99.58	99.16
Oats	20	7	93	46.99	47.52	75.94	57.46	62.42	22.69	39.34	89.47	94.07
Soybeans-no till	968	7	93	69.29	68.28	67.89	83.05	84.41	87.59	86.21	87.14	92.83
Soybeans-min till	2468	7	93	85.12	84.43	86.88	91.70	93.68	97.71	97.51	95.96	98.51
Soybeans-clean till	614	7	93	79.40	78.41	74.90	87.71	90.03	95.67	95.88	95.24	96.58
Wheat	212	7	93	95.98	96.53	97.00	97.38	97.58	99.95	99.60	99.34	98.38
Woods	1294	7	93	97.67	97.97	98.19	98.08	98.59	99.94	99.81	98.84	99.01
Bldg-Grass-Tree	380	7	93	45.94	43.59	68.51	64.42	74.86	60.16	61.26	91.16	78.77
Stone-steeltowers	95	7	93	76.42	76.16	71.16	76.42	81.82	93.35	97.73	83.37	82.04
OA /%	-	-	-	80.93	80.46	82.82	88.99	91.08	92.99	92.83	93.62	96.16
Kappa	-	-	-	78.12	77.58	80.28	87.41	89.81	91.96	91.78	92.11	95.62

Table 1. Classification data statistics of Indian Pines data sets

Groundtruth	Sum	Train /%	Test /%	SVM /%	SVM-PCA /%	GBF-SVM /%	BF-SVM /%	GDF-SVM /%	EPF-B-g /%	EPF-G-g /%	IFRF /%	AMF-SVM /%
Asphalt	6641	2	98	87.84	86.19	88.74	88.23	94.98	98.07	97.49	97.70	98.68
Meadows	18649	2	98	95.81	95.99	96.13	97.03	98.32	99.98	99.91	99.34	99.79
Gravel	2099	2	98	57.87	48.76	54.51	65.01	76.07	72.60	69.39	86.68	90.63
Trees	3064	2	98	88.17	85.01	89.21	91.98	96.19	91.84	92.26	92.78	96.56
Metalsheets	1345	2	98	98.34	98.72	98.84	97.54	98.38	99.85	99.94	99.02	99.40
Soil	5029	2	98	54.33	54.96	56.21	77.91	88.34	60.74	60.32	99.86	97.59
Bitumen	1330	2	98	64.64	64.79	65.89	70.50	82.89	81.27	86.38	96.37	95.12
Bricks	3682	2	98	78.97	79.41	77.88	80.18	91.43	98.47	95.95	73.13	97.05
Shadows	947	2	98	89.33	84.29	90.64	87.82	93.37	95.13	93.20	83.10	94.49
OA /%	-	-	-	84.80	83.96	85.20	89.03	94.20	92.32	91.92	95.31	98.17
Kappa	-	-	-	79.47	78.31	80.00	85.34	92.29	89.57	89.04	93.67	97.57

Table 2. Classification statistics of Pavia University data sets

Index	n	0	1	2	3	4	5	6	7	8
	Tree height	2	3	4	5	6	7	8	9	10
	Tree node	3	7	15	31	63	127	255	511	1023
Indian Pines	OA /%	95.61	95.67	95.97	95.61	95.80	96.15	96.04	96.16	96.13
Indian Pines	Kappa	94.98	95.05	95.39	94.99	95.20	95.61	95.47	95.62	95.58
Pavia	OA /%	98.02	98.19	98.14	98.20	98.08	98.17	98.40	98.29	98.17
Pavia	Kappa	97.37	97.60	97.53	97.62	97.45	97.58	97.88	97.73	97.57

Table 3. Hyperspectral classification data statistics of adaptive manifold filtering

Jianshang Liao, Liguo Wang, Siyuan Hao. Hyperspectral Image Classification Method Based on Adaptive Manifold Filtering[J]. Laser & Optoelectronics Progress, 2018, 55(4): 041010

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